Gordon L. Kindlmann recently joined the University of Chicago as an Assistant Professor in the Department of Computer Science, and as a Fellow in the Computation Institute. In 2008 he finished his post-doctoral studies in the Laboratory of Mathematics in Imaging under Carl-Fredrik Westin (Dept of Radiology, Brigham & Women's Hospital, Harvard Medical School), where he continued the research on diffusion tensor visualization and analysis that was initiated during his PhD (completed 2004) under Christopher Johnson at the University of Utah Scientific Computing and Imaging Institute. His introduction to visualization came through his Master's thesis work on transfer function design for volume rendering with Donald Greenberg at the Cornell University Program of Computer Graphics (MS 1999), as well as some experiments in polytope visualization during his undergraduate mathematics studies at Cornell University (BA 1995).

AG 4   Info

AG Audience

English

45 Minutes

Saarbrücken

Particle systems have gained importance as a methodology for sampling implicit surfaces and segmented objects to improve mesh generation and shape analysis. We propose that particle systems have a significantly more general role in extracting structure from *unsegmented* data. We describe a particle system that computes samplings of crease features (i.e. ridges and valleys, as lines or surfaces) that effectively represent many anatomical structures in scanned medical data. Because structure naturally exists at a range of sizes relative to the image resolution, computer vision has developed the theory of scale-space, which considers an N-D image as an (N+1)-D stack of images at varying blurring levels. Our scale-space particles move through continuous 4-D scale-space according to spatial constraints imposed by the crease features, a particle-image energy that draws particles towards scales of maximal feature strength, and an inter-particle energy that controls sampling density in space and scale. To make scale-space practical for large three-dimensional data, we present a spline-based interpolation across scale from an optimal sparse set of pre-computed blurrings. The configuration of the particle system is visualized with tensor glyphs that display information about the local Hessian of the image, and the scale of the particle. We use scale-space particles to sample the complex three-dimensional branching structure of airways in lung CT, and then begin investigating the scale-space structure of diffusion tensor imaging by using creases of diffusion anisotropy to capture major white matter structure in the brain.

Thorsten Thormählen

+49 681 9325-417

--email hidden

passcode not visible

logged in users only